1. Field of the Invention
The invention relates to information recording and reproducing apparatus and method of a magnetic disk an MO, an optical disk, a magnetic tape, or the like and, more particularly, to information recording and reproducing apparatus and method for decoding by applying noise characteristics of a magnetic recording and reproducing system to an iterative decoding.
2. Description of the Related Arts
Hitherto, in order to reproduce a recorded signal without an error, a powerful error correcting function is installed in a recording and reproducing apparatus. The recording signal can be certainly restored from an unstable signal including noises by the error correction.
In recent years, the error correction of the recording and reproducing apparatus is realized mainly by a combination of two methods called PRML (Partial Response Maximum Likelihood) and ECC (Error Correction Code). The PRML is a method whereby a recording channel is regarded as a partial response channel (hereinafter, referred to as a PR channel) with an intersymbol interference and a Maximum Likelihood Decoding generally using a viterbi detector is executed.
In recent years, as a new encoding/decoding method in place of the PRML, a turbo encoding, an LDPC (Low Density Parity Check Code), or the like has been proposed. Those methods are generally referred to as an iterative decoding method here because the decoding is performed by an iterative calculation. As an iterative decoding method, the turbo encoding disclosed in the drawings of the specification of the U.S. Pat. No. 5,446,747 is a typical method. The turbo encoding is a parallel concatenation encoding in which two RSC (Recursive Systematic Convolutional Codes) are connected through a random interleaver; and the decoding is executed by an iterative calculation using two soft input/output decoders. Although the turbo encoding has been devised in the field of communication, in case of applying it to the PR channel of a magnetic recording and reproducing system, two constituent encoders are serially concatenated through the random interleaver. At this time, the constituent encoder near the channel is called an inner encoder and the other constituent encoder is called an outer encoder. In the PR channel, since the channel itself can be regarded as a convolutional encoder, there is no need to particularly provide the inner encoder. As an outer encoder, various outer encoders such as encoder using two RSCs, encoder using one RSC, and the like have been provided. There is also a case using an LDPC (Low Density Parity Check Code) disclosed in the literature, R. G. Gallager, “Low-Density Parity-Check Codes”, Cambridge, Mass.: MIT Press, 1963.
A decoder of the iterative decoding method is constructed by two constituent decoders called an inner decoder and an outer decoder. It is a characteristic point of the iterative decoding method that an MAP (Maximum A posteriori Probability) decoding is executed. For this purpose, each of the two constituent decoders becomes an SISO (Soft-In/Soft-Out) decoder. The SISO decoder does not output a hard decision result of mere “0” or “1” but outputs reliability information such as 0.4 or 0.9. There is a BCJR (Bahl-Cocke-Jeinek-Raviv) algorithm as a specific calculating method of the soft-in soft-out (SISO) decoding for a code such as a convolutional code or the like which is defined by the state transition. The BCJR algorithm has been described in detail in the literature, L. R. Bahl et al., “Optimal decoding of linear codes for minimizing symbol error rate”, IEEE Trans. Inform. Theory, Vol.20, pp. 248–287, 1974. Such an iterative decoding method has a high error correcting ability exceeding that of the PRML decoding method and is regarded as a useful encoding/decoding method of the next generation.
In the information recording and reproducing apparatus, if noise characteristics of a magnetic recording and reproducing channel are accurately predicted and applied to a decoding step, error rate performance can be improved. A decoding method serving as a base as a noise prediction scheme in the conventional information recording and reproducing apparatus is a Maximum Likelihood decoding method. For example, such decoding methods have been disclosed in the drawings of the specification of U.S. Pat. No. 6,158,027, the drawings of the specification of U.S. Pat. No. 6,104,766, the drawings of the specification of U.S. Pat. No. 5,784,415, the drawings of the specification of EPC. Patent No. WO9852330, and the like. However, according to those patents, the noise prediction scheme is not applied to an iterative decoding method such as MAP (Maximum A posteriori Probability) decoding method, turbo decoding method, LDPC (Low Density Parity Check Code) encoding method, or the like. In those patents, no consideration is made with respect to input signal pattern dependency of a noise correlation and a handling as a noise model is extremely insufficient.
On the other hand, in the paper, A. Kavcic and A. Patapoutian, “A signal-dependent autoregressive channel models”, IEEE Trans. Magn., Vol. 35, No. 5, pp. 2316–2318, September 1999 or the paper, A. Kavcic, “Soft-Output Detector for Channels with Intersymbol Interference and Markov Noise Memory”, Proc. IEEE Global Telecom. Conf., December, 1999, a point that a correlation to the past noises depend on an input signal pattern was discussed for the first time, and this theory is applied to a Viterbi algorithm or the MAP algorithm. However, nothing is considered with respect to a correlation to future noises. In the paper, Y. Wu and J. R. Cruz, “Noise predictive turbo systems”, TMRC'2000 Paper E5, August 2000 or the paper, T. R. Oenning, “Channel capacity and coding for magnetic recording channels with media noise”, PhD thesis, the University of Minnesota, September 2000, a method whereby a countermeasure method against the noise correlation based on the noise prediction scheme is applied to the iterative decoding method was discussed for the first time. However, nothing is considered with respect to a point that the noise correlation depends on a pattern of an input signal, and a handling of a noise model is insufficient.